Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("tong.Coggan2024_behavior-ConditionWiseAccuracySimilarity")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.844
2	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.779
3	convnext_large_imagenet_full_seed-0	.700
4	vit_base_patch16_clip_224:openai_ft_in1k	.677
5	cvt_cvt-21-384-in1k_4_LucyV4	.659
6	convnext_xlarge:fb_in22k_ft_in1k	.636
7	focalnet_tiny_lrf_in1k	.628
8	cvt_cvt-21-224-in1k_4_LucyV4	.626
9	convnext_base_imagenet_full_seed-0	.619
10	convnext_tiny:in12k_ft_in1k	.613
11	vit_relpos_base_patch16_clsgap_224:sw_in1k	.611
12	convnext_large:fb_in22k_ft_in1k	.603
13	AdvProp_efficientnet-b7	.597
14	tv_efficientnet-b1	.595
15	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.591
16	resnet50-vitoimagevidnet8	.590
17	resnet50-VITO-8deg-cc	.590
18	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.588
19	resnet101_ecoset_full	.588
20	cvt_cvt-13-224-in1k_4_LucyV4	.586
21	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.575
22	effnetb1_272x240	.573
23	resnet152_ecoset_full	.571
24	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.566
25	AdvProp_efficientnet-b6	.558
26	omnivore_swinS	.555
27	resnext101_32x8d_wsl	.554
28	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.552
29	AdvProp_efficientnet-b2	.552
30	AT_efficientnet-b2	.552
31	efficientnet-b7	.551
32	ViT_L_32_imagenet1k	.542
33	resnet_101_v2	.541
34	convnext_small_imagenet_full_seed-0	.540
35	resnext101_32x48d_wsl	.536
36	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.535
37	resnext101_32x16d_wsl	.534
38	convnext_tiny_imagenet_full_seed-0	.529
39	vit_large_patch14_clip_224:openai_ft_in1k	.526
40	deit_large_imagenet_full_seed-0	.523
41	convnext_tiny_sup	.519
42	resnet_50_v2	.515
43	resnet50-moclr8deg	.515
44	vit_large_patch14_clip_224:laion2b_ft_in1k	.505
45	resnet50_ecoset_full	.504
46	cvt_cvt-13-384-in1k_4_LucyV4	.501
47	AdvProp_efficientnet-b8	.497
48	BiT-S-R50x1	.482
49	resnet_152_v2	.478
50	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.476
51	deit_base_imagenet_full_seed-0	.471
52	resnext101_32x32d_wsl	.469
53	resnet-152_v2_pytorch	.466
54	efficientnet_b0	.463
55	resnet152_imagenet_full	.459
56	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.457
57	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.453
58	AdvProp_efficientnet-b4	.448
59	deit_small_imagenet_full_seed-0	.448
60	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.446
61	convnext_femto_ols:d1_in1k	.434
62	inception_v3	.430
63	grcnn_109	.425
64	voneresnet-50	.421
65	densenet_201_pytorch	.412
66	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.411
67	antialiased-r50	.409
68	resnet50_tutorial	.407
69	voneresnet-50-non_stochastic	.398
70	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.393
71	nasnet_large	.383
72	BiT-S-R101x1	.379
73	convnext_small_imagenet_100_seed-0	.374
74	inception_v4	.372
75	blt_vs	.372
76	Res2Net50_26w_4s	.371
77	resnet_152_v1	.371
78	inception_v3_pytorch	.360
79	yudixie_resnet50_imagenet1kpret_0_240908	.359
80	resnet18_ecoset_full	.356
81	resnet_101_v1	.351
82	resnet34_ecoset_full	.342
83	resnet18_imagenet21kP	.338
84	resnet50-sup	.331
85	resnet_50_v1	.331
86	grcnn	.331
87	resnet101_imagenet_full	.329
88	mobilenet_v2_1_0_224	.326
89	xception	.323
90	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.320
91	cv_18_dagger_408_pretrained	.320
92	resnet_SIN_IN_FT_IN	.317
93	vonegrcnn_52e_full	.317
94	resnet50_imagenet_full	.316
95	BiT-S-R152x2	.315
96	antialias-resnet152	.312
97	inception_v1	.311
98	deit_small_imagenet_100_seed-0	.305
99	mobilenet_v2_1_0_128	.303
100	vonegrcnn_62e_nobn	.303
101	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.300
102	vonegrcnn_47e	.299
103	VOneCORnet-S	.298
104	densenet-201	.295
105	resnet50-SIN_IN	.293
106	custom_model_cv_18_dagger_408	.288
107	resnet50-SIN_IN_IN	.284
108	resnet34_imagenet_full	.277
109	vit_large_patch14_clip_336:laion2b_ft_in1k	.273
110	resnet50-barlow	.258
111	nasnet_mobile	.256
112	regnet_y_400mf	.251
113	efficientnet_b0_imagenet_full	.236
114	mobilenet_v2_0_75_160	.235
115	efficientnet_b2_imagenet_full	.229
116	densenet-121	.226
117	resnet18_imagenet_full	.215
118	mobilenet_v2_1_0_192	.214
119	mobilenet_v2_0_75_192	.211
120	mobilenet_v2_1_0_160	.211
121	vgg_19	.191
122	resnet50_robust_l2_eps1	.180
123	ReAlnet02_cornet	.180
124	ReAlnet02	.180
125	vgg_16	.166
126	shufflenet_v2_x1_0	.165
127	resnet50-vicregl0p9	.162
128	alexnet_training_seed_06	.162
129	yudixie_resnet18_imagenet1kpret_0_240719	.159
130	efficientnet_b1_imagenet_full	.156
131	ReAlnet07	.154
132	ReAlnet07_cornet	.154
133	mobilenet_v2_1_4_224	.144
134	mobilenet_v2_1-4_224_pytorch	.144
135	resnet-50-robust	.143
136	resnet50-SIN	.142
137	yudixie_resnet50_category_class_0_240908	.139
138	ReAlnet01_cornet	.133
139	ReAlnet01	.133
140	ReAlnet_incorrect	.133
141	resnet-18_test_m	.132
142	alexnet_ks_torevert	.132
143	resnet50-vicreg	.129
144	ReAlnet05	.129
145	ReAlnet05_cornet	.129
146	ReAlnet08_cornet	.113
147	ReAlnet08	.113
148	yudixie_resnet18_object_class_0_240719	.113
149	ReAlnet03	.111
150	ReAlnet03_cornet	.111
151	imagenet_l2_3_0	.110
152	resnet50_robust_l2_eps3	.110
153	resnet50-vicregl0p75	.108
154	ReAlnet10_cornet	.101
155	ReAlnet10	.101
156	pnasnet_large_pytorch	.099
157	ReAlnet06	.096
158	ReAlnet06_cornet	.096
159	resnet50_eMMCR_Vanilla	.095
160	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.092
161	ReAlnet09_cornet	.092
162	ReAlnet09	.092
163	AlexNet_SIN	.089
164	ReAlnet04	.079
165	ReAlnet04_cornet	.079
166	yudixie_resnet18_category_class_0_240719	.077
167	mobilenet_v2_0_5_192	.077
168	alexnet_training_seed_03	.063
169	resnet50_imagenet_100_seed-0	.058
170	alexnet_training_seed_01	.052
171	alexnet_training_seed_02	.038
172	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.036
173	yudixie_resnet50_object_class_0_240908	.023
174	alexnet_training_seed_07	.023
175	alexnet_training_seed_05	.020
176	alexnet_training_seed_10	.018
177	alexnet_training_seed_09	.012
178	alexnet_training_seed_04	.007
179	yudixie_resnet50_rotation_reg_0_240908	.007
180	yudixie_resnet50_random_0_240908	.006
181	deit_small_imagenet_1_seed-0	.006
182	alexnet_training_seed_08	.005
183	resnet50_imagenet_10_seed-0	.002
184	yudixie_resnet50_distance_rotation_0_240908	.002
185	yudixie_resnet18_random_0_240719	.001
186	yudixie_resnet50_distance_translation_0_240908	.000
187	yudixie_resnet50_distance_reg_0_240908	.000
188	yudixie_resnet18_rotation_reg_0_240719	.000
189	convnext_small_imagenet_1_seed-0	.002
190	yudixie_resnet18_translation_rotation_0_240719	.002
191	deit_small_imagenet_10_seed-0	.002
192	resnet50_imagenet_1_seed-0	.002
193	yudixie_resnet18_distance_reg_0_240719	.006
194	yudixie_resnet18_distance_rotation_0_240719	.007
195	yudixie_resnet50_distance_translation_rotation_0_240908	.008
196	yudixie_resnet18_translation_reg_0_240719	.009
197	yudixie_resnet50_translation_rotation_0_240908	.017
198	yudixie_resnet50_translation_reg_0_240908	.023
199	yudixie_resnet18_distance_translation_rotation_0_240719	.023
200	yudixie_resnet18_distance_translation_0_240719	.033
201	convnext_small_imagenet_10_seed-0	.034
202	artResNet18_1	.067
203	hmax	.142
204	BiT-S-R152x4	X
205	mobilenet_v2_0_75_224	X
206	voneresnet-50-robust	X
207	antialiased-rnext101_32x8d	X
208	ConvLSTM	X
209	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	X
210	SimVP	X
211	TAU	X
212	mobilevit_small	X
213	BiT-S-R50x3	X
214	densenet-169	X
215	pnasnet_large	X
216	PredRNN	X
217	MIM	X
218	convnext_base:clip_laiona_augreg_ft_in1k_384	X
219	mobilenet_v2_1_3_224	X
220	omnivore_swinB	X
221	mobilenet_v2_0_5_224	X
222	BiT-S-R101x3	X

Benchmark bibtex

None

Ceiling

Not available

Data: tong.Coggan2024_behavior

Metric: ConditionWiseAccuracySimilarity